def run(args):
files = glob(args.file_regex)
out_dir = os.path.abspath(args.out_dir)
logger = Logger(out_dir,
active_file='extraction_log',
overwrite_existing=args.overwrite,
print_calling_method=False)
logger("Args dump: {}".format(vars(args)))
logger("Found {} files matching glob statement".format(len(files)))
if len(files) == 0:
return
channels = ChannelMontageTuple(args.channels, relax=True)
renamed_channels = args.rename_channels
if renamed_channels and (len(renamed_channels) != len(channels)):
raise ValueError("--rename_channels argument must have the same number"
" of elements as --channels. Got {} and {}.".format(
len(channels), len(renamed_channels)
))
logger("Extracting channels {}".format(channels.names))
if renamed_channels:
logger("Saving channels under names {}".format(renamed_channels))
logger("Saving .h5 files to '{}'".format(out_dir))
logger("Re-sampling: {}".format(args.resample))
logger("-*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*-")
extract(
files=files,
out_dir=out_dir,
channels=channels,
renamed_channels=renamed_channels,
logger=logger,
args=args
)
def get_logger(out_dir, overwrite, name="evaluation_log"):
"""
Returns a Logger object for the given out_dir.
The logger will throw an OSError if the dir exists and overwrite=False, in
which case the script will terminate with a print message.
"""
from mpunet.logging import Logger
try:
logger = Logger(out_dir,
active_file=name,
overwrite_existing=overwrite,
no_sub_folder=True)
except OSError:
from sys import exit
print("[*] A logging file 'logs/{}' already exists. "
"If you wish to overwrite this logfile, set the --overwrite "
"flag.".format(name))
exit(0)
return logger
def get_logger(project_dir, overwrite_existing):
"""
Initialises and returns a Logger object for a given project directory.
If a logfile already exists at the specified location, it will be
overwritten if continue_training == True, otherwise raises RuntimeError
Args:
project_dir: Path to a mpunet project folder
overwrite_existing: Whether to overwrite existing logfile in project_dir
Returns:
A mpunet Logger object initialized in project_dir
"""
# Define Logger object
from mpunet.logging import Logger
try:
logger = Logger(base_path=project_dir,
print_to_screen=True,
overwrite_existing=overwrite_existing)
except OSError as e:
raise RuntimeError("[*] A training session at '%s' already exists."
"\n Use the --overwrite flag to "
"overwrite." % project_dir) from e
return logger
def entry_func(args=None):
# Get parser
parser = vars(get_parser().parse_args(args))
# Get parser arguments
cv_dir = os.path.abspath(parser["CV_dir"])
out_dir = os.path.abspath(parser["out_dir"])
create_folders(out_dir)
await_PID = parser["wait_for"]
run_split = parser["run_on_split"]
start_from = parser["start_from"] or 0
num_jobs = parser["num_jobs"] or 1
# GPU settings
num_GPUs = parser["num_GPUs"]
force_GPU = parser["force_GPU"]
ignore_GPU = parser["ignore_GPU"]
monitor_GPUs_every = parser["monitor_GPUs_every"]
# User input assertions
_assert_force_and_ignore_gpus(force_GPU, ignore_GPU)
if run_split:
_assert_run_split(start_from, monitor_GPUs_every, num_jobs)
# Wait for PID?
if await_PID:
from mpunet.utils import await_PIDs
await_PIDs(await_PID)
# Get file paths
script = os.path.abspath(parser["script_prototype"])
hparams = os.path.abspath(parser["hparams_prototype"])
no_hparams = parser["no_hparams"]
# Get list of folders of CV data to run on
cv_folders = get_CV_folders(cv_dir)
if run_split is not None:
if run_split < 0 or run_split >= len(cv_folders):
raise ValueError("--run_on_split should be in range [0-{}], "
"got {}".format(len(cv_folders) - 1, run_split))
cv_folders = [cv_folders[run_split]]
log_appendix = "_split{}".format(run_split)
else:
log_appendix = ""
# Get a logger object
logger = Logger(base_path="./",
active_file="output" + log_appendix,
print_calling_method=False,
overwrite_existing=True)
if force_GPU:
# Only these GPUs fill be chosen from
from mpunet.utils import set_gpu
set_gpu(force_GPU)
if num_GPUs:
# Get GPU sets (up to the number of splits)
gpu_sets = get_free_GPU_sets(num_GPUs, ignore_GPU)[:len(cv_folders)]
elif not num_jobs or num_jobs < 0:
raise ValueError("Should specify a number of jobs to run in parallel "
"with the --num_jobs flag when using 0 GPUs pr. "
"process (--num_GPUs=0 was set).")
else:
gpu_sets = ["''"] * parser["num_jobs"]
# Get process pool, lock and GPU queue objects
lock = Lock()
gpu_queue = Queue()
for gpu in gpu_sets:
gpu_queue.put(gpu)
procs = []
if monitor_GPUs_every is not None and monitor_GPUs_every:
logger("\nOBS: Monitoring GPU pool every %i seconds\n" %
monitor_GPUs_every)
# Start a process monitoring new GPU availability over time
stop_event = Event()
t = Process(target=monitor_GPUs,
args=(monitor_GPUs_every, gpu_queue, num_GPUs, ignore_GPU,
gpu_sets, stop_event))
t.start()
procs.append(t)
else:
stop_event = None
try:
for cv_folder in cv_folders[start_from:]:
gpus = gpu_queue.get()
t = Process(target=run_sub_experiment,
args=(cv_folder, out_dir, script, hparams, no_hparams,
gpus, gpu_queue, lock, logger))
t.start()
procs.append(t)
for t in procs:
if not t.is_alive():
t.join()
except KeyboardInterrupt:
for t in procs:
t.terminate()
if stop_event is not None:
stop_event.set()
for t in procs:
t.join()
def entry_func(args=None):
# Project base path
args = vars(get_argparser().parse_args(args))
basedir = os.path.abspath(args["project_dir"])
overwrite = args["overwrite"]
continue_training = args["continue_training"]
eval_prob = args["eval_prob"]
await_PID = args["wait_for"]
dice_weight = args["dice_weight"]
print("Fitting fusion model for project-folder: %s" % basedir)
# Minimum images in validation set before also using training images
min_val_images = 15
# Fusion model training params
epochs = args['epochs']
fm_batch_size = args["batch_size"]
# Early stopping params
early_stopping = args["early_stopping"]
# Wait for PID?
if await_PID:
from mpunet.utils import await_PIDs
await_PIDs(await_PID)
# Fetch GPU(s)
num_GPUs = args["num_GPUs"]
force_gpu = args["force_GPU"]
# Wait for free GPU
if not force_gpu:
await_and_set_free_gpu(N=num_GPUs, sleep_seconds=120)
else:
set_gpu(force_gpu)
# Get logger
logger = Logger(base_path=basedir,
active_file="train_fusion",
overwrite_existing=overwrite)
# Get YAML hyperparameters
hparams = YAMLHParams(os.path.join(basedir, "train_hparams.yaml"))
# Get some key settings
n_classes = hparams["build"]["n_classes"]
if hparams["build"]["out_activation"] == "linear":
# Trained with logit targets?
hparams["build"][
"out_activation"] = "softmax" if n_classes > 1 else "sigmoid"
# Get views
views = np.load("%s/views.npz" % basedir)["arr_0"]
del hparams["fit"]["views"]
# Get weights and set fusion (output) path
weights = get_best_model("%s/model" % basedir)
weights_name = os.path.splitext(os.path.split(weights)[-1])[0]
fusion_weights = "%s/model/fusion_weights/" \
"%s_fusion_weights.h5" % (basedir, weights_name)
create_folders(os.path.split(fusion_weights)[0])
# Log a few things
log(logger, hparams, views, weights, fusion_weights)
# Check if exists already...
if not overwrite and os.path.exists(fusion_weights):
from sys import exit
print("\n[*] A fusion weights file already exists at '%s'."
"\n Use the --overwrite flag to overwrite." % fusion_weights)
exit(0)
# Load validation data
images = ImagePairLoader(**hparams["val_data"], logger=logger)
is_validation = {m.identifier: True for m in images}
# Define random sets of images to train on simul. (cant be all due
# to memory constraints)
image_IDs = [m.identifier for m in images]
if len(images) < min_val_images:
# Pick N random training images
diff = min_val_images - len(images)
logger("Adding %i training images to set" % diff)
# Load the training data and pick diff images
train = ImagePairLoader(**hparams["train_data"], logger=logger)
indx = np.random.choice(np.arange(len(train)),
diff,
replace=diff > len(train))
# Add the images to the image set set
train_add = [train[i] for i in indx]
for m in train_add:
is_validation[m.identifier] = False
image_IDs.append(m.identifier)
images.add_images(train_add)
# Append to length % sub_size == 0
sub_size = args["images_per_round"]
rest = int(sub_size * np.ceil(len(image_IDs) / sub_size)) - len(image_IDs)
if rest:
image_IDs += list(np.random.choice(image_IDs, rest, replace=False))
# Shuffle and split
random.shuffle(image_IDs)
sets = [
set(s) for s in np.array_split(image_IDs,
len(image_IDs) / sub_size)
]
assert (contains_all_images(sets, image_IDs))
# Define fusion model (named 'org' to store reference to orgiginal model if
# multi gpu model is created below)
fusion_model = FusionModel(n_inputs=len(views),
n_classes=n_classes,
weight=dice_weight,
logger=logger,
verbose=False)
if continue_training:
fusion_model.load_weights(fusion_weights)
print("\n[OBS] CONTINUED TRAINING FROM:\n", fusion_weights)
import tensorflow as tf
with tf.distribute.MirroredStrategy().scope():
# Define model
unet = init_model(hparams["build"], logger)
print("\n[*] Loading weights: %s\n" % weights)
unet.load_weights(weights, by_name=True)
# Compile the model
logger("Compiling...")
metrics = [
"sparse_categorical_accuracy", sparse_fg_precision, sparse_fg_recall
]
fusion_model.compile(optimizer=Adam(lr=1e-3),
loss=fusion_model.loss,
metrics=metrics)
fusion_model._log()
try:
_run_fusion_training(sets, logger, hparams, min_val_images,
is_validation, views, n_classes, unet,
fusion_model, early_stopping, fm_batch_size,
epochs, eval_prob, fusion_weights)
except KeyboardInterrupt:
pass
finally:
if not os.path.exists(os.path.split(fusion_weights)[0]):
os.mkdir(os.path.split(fusion_weights)[0])
# Save fusion model weights
# OBS: Must be original model if multi-gpu is performed!
fusion_model.save_weights(fusion_weights)
def run(args, gpu_mon):
"""
Run the script according to args - Please refer to the argparser.
args:
args: (Namespace) command-line arguments
gpu_mon: (GPUMonitor) Initialized mpunet GPUMonitor object
"""
assert_args(args)
from mpunet.logging import Logger
from utime.train import Trainer
from utime.hyperparameters import YAMLHParams
from utime.utils.scriptutils import (assert_project_folder,
make_multi_gpu_model)
from utime.utils.scriptutils.train import (get_train_and_val_datasets,
get_h5_train_and_val_datasets,
get_data_queues, get_generators,
find_and_set_gpus,
get_samples_per_epoch,
save_final_weights)
project_dir = os.path.abspath("./")
assert_project_folder(project_dir)
if args.overwrite and not args.continue_training:
from mpunet.bin.train import remove_previous_session
remove_previous_session(project_dir)
# Get logger object
logger = Logger(project_dir,
overwrite_existing=args.overwrite,
append_existing=args.continue_training,
log_prefix=args.log_file_prefix)
logger("Args dump: {}".format(vars(args)))
# Settings depending on --preprocessed flag.
if args.preprocessed:
yaml_path = utime.Defaults.get_pre_processed_hparams_path(project_dir)
dataset_func = get_h5_train_and_val_datasets
train_queue_type = 'eager'
val_queue_type = 'eager'
else:
yaml_path = utime.Defaults.get_hparams_path(project_dir)
dataset_func = get_train_and_val_datasets
train_queue_type = args.train_queue_type
val_queue_type = args.val_queue_type
# Load hparams
hparams = YAMLHParams(yaml_path, logger=logger)
update_hparams_with_command_line_arguments(hparams, args)
# Initialize and load (potentially multiple) datasets
train_datasets, val_datasets = dataset_func(hparams, args.no_val,
args.train_on_val, logger)
if args.just:
keep_n_random(*train_datasets,
*val_datasets,
keep=args.just,
logger=logger)
# Get a data loader queue object for each dataset
train_datasets_queues = get_data_queues(
datasets=train_datasets,
queue_type=train_queue_type,
max_loaded_per_dataset=args.max_loaded_per_dataset,
num_access_before_reload=args.num_access_before_reload,
logger=logger)
if val_datasets:
val_dataset_queues = get_data_queues(
datasets=val_datasets,
queue_type=val_queue_type,
max_loaded_per_dataset=args.max_loaded_per_dataset,
num_access_before_reload=args.num_access_before_reload,
study_loader=getattr(train_datasets_queues[0], 'study_loader',
None),
logger=logger)
else:
val_dataset_queues = None
# Get sequence generators for all datasets
train_seq, val_seq = get_generators(train_datasets_queues,
val_dataset_queues=val_dataset_queues,
hparams=hparams)
# Add additional (inferred) parameters to parameter file
hparams.set_value("build",
"n_classes",
train_seq.n_classes,
overwrite=True)
hparams.set_value("build",
"batch_shape",
train_seq.batch_shape,
overwrite=True)
hparams.save_current()
if args.continue_training:
# Prepare the project directory for continued training.
# Please refer to the function docstring for details
from utime.models.model_init import prepare_for_continued_training
parameter_file = prepare_for_continued_training(
hparams=hparams, project_dir=project_dir, logger=logger)
else:
parameter_file = args.initialize_from # most often is None
# Set the GPU visibility
num_GPUs = find_and_set_gpus(gpu_mon, args.force_GPU, args.num_GPUs)
# Initialize and potential load parameters into the model
from utime.models.model_init import init_model, load_from_file
org_model = init_model(hparams["build"], logger)
if parameter_file:
load_from_file(org_model, parameter_file, logger, by_name=True)
model, org_model = make_multi_gpu_model(org_model, num_GPUs)
# Prepare a trainer object. Takes care of compiling and training.
trainer = Trainer(model, org_model=org_model, logger=logger)
import tensorflow as tf
trainer.compile_model(n_classes=hparams["build"].get("n_classes"),
reduction=tf.keras.losses.Reduction.NONE,
**hparams["fit"])
# Fit the model on a number of samples as specified in args
samples_pr_epoch = get_samples_per_epoch(train_seq,
args.max_train_samples_per_epoch)
_ = trainer.fit(train=train_seq,
val=val_seq,
train_samples_per_epoch=samples_pr_epoch,
**hparams["fit"])
# Save weights to project_dir/model/{final_weights_file_name}.h5
# Note: these weights are rarely used, as a checkpoint callback also saves
# weights to this directory through training
save_final_weights(project_dir,
model=model,
file_name=args.final_weights_file_name,
logger=logger)
def run(args, gpu_mon):
"""
Run the script according to args - Please refer to the argparser.
args:
args: (Namespace) command-line arguments
gpu_mon: (GPUMonitor) Initialized MultiPlanarUNet GPUMonitor object
"""
assert_args(args)
from mpunet.logging import Logger
from utime.train import Trainer
from utime.hyperparameters import YAMLHParams
from utime.utils.scriptutils import (assert_project_folder,
make_multi_gpu_model)
from utime.utils.scriptutils.train import (get_train_and_val_datasets,
get_generators,
find_and_set_gpus,
get_samples_per_epoch,
save_final_weights)
project_dir = os.path.abspath("./")
assert_project_folder(project_dir)
if args.overwrite and not args.continue_training:
from mpunet.bin.train import remove_previous_session
remove_previous_session(project_dir)
# Get logger object
logger = Logger(project_dir,
overwrite_existing=args.overwrite
or args.continue_training,
log_prefix=args.log_file_prefix)
logger("Args dump: {}".format(vars(args)))
# Load hparams
hparams = YAMLHParams(os.path.join(project_dir, "hparams.yaml"),
logger=logger)
update_hparams_with_command_line_arguments(hparams, args)
# Initialize and load (potentially multiple) datasets
datasets, no_val = get_train_and_val_datasets(hparams, args.no_val,
args.train_on_val, logger)
# Load data in all datasets
for data in datasets:
for d in data:
d.load(1 if args.just_one else None)
d.pairs = d.loaded_pairs # remove the other pairs
# Get sequence generators for all datasets
train_seq, val_seq = get_generators(datasets, hparams, no_val)
# Add additional (inferred) parameters to parameter file
hparams.set_value("build",
"n_classes",
train_seq.n_classes,
overwrite=True)
hparams.set_value("build",
"batch_shape",
train_seq.batch_shape,
overwrite=True)
hparams.save_current()
if args.continue_training:
# Prepare the project directory for continued training.
# Please refer to the function docstring for details
from utime.models.model_init import prepare_for_continued_training
parameter_file = prepare_for_continued_training(
hparams=hparams, project_dir=project_dir, logger=logger)
else:
parameter_file = args.initialize_from # most often is None
# Set the GPU visibility
num_GPUs = find_and_set_gpus(gpu_mon, args.force_GPU, args.num_GPUs)
# Initialize and potential load parameters into the model
from utime.models.model_init import init_model, load_from_file
org_model = init_model(hparams["build"], logger)
if parameter_file:
load_from_file(org_model, parameter_file, logger, by_name=True)
model, org_model = make_multi_gpu_model(org_model, num_GPUs)
# Prepare a trainer object. Takes care of compiling and training.
trainer = Trainer(model, org_model=org_model, logger=logger)
trainer.compile_model(n_classes=hparams["build"].get("n_classes"),
**hparams["fit"])
# Fit the model on a number of samples as specified in args
samples_pr_epoch = get_samples_per_epoch(train_seq,
args.max_train_samples_per_epoch,
args.val_samples_per_epoch)
_ = trainer.fit(train=train_seq,
val=val_seq,
train_samples_per_epoch=samples_pr_epoch[0],
val_samples_per_epoch=samples_pr_epoch[1],
**hparams["fit"])
# Save weights to project_dir/model/{final_weights_file_name}.h5
# Note: these weights are rarely used, as a checkpoint callback also saves
# weights to this directory through training
save_final_weights(project_dir,
model=model,
file_name=args.final_weights_file_name,
logger=logger)
def run(args):
cv_dir = os.path.abspath(args.CV_dir)
# Get list of folders of CV data to run on
cv_folders = get_CV_folders(cv_dir)
assert_args(args, n_splits=len(cv_folders))
out_dir = os.path.abspath(args.out_dir)
hparams_dir = os.path.abspath(args.hparams_prototype_dir)
prepare_hparams_dir(hparams_dir)
create_folders(out_dir)
# Wait for PID?
if args.wait_for:
from mpunet.utils import await_PIDs
await_PIDs(args.wait_for)
if args.run_on_split is not None:
cv_folders = [cv_folders[args.run_on_split]]
log_appendix = "_split{}".format(args.run_on_split)
else:
log_appendix = ""
# Get a logger object
logger = Logger(base_path="./",
active_file="output" + log_appendix,
print_calling_method=False,
overwrite_existing=True)
if args.force_GPU:
# Only these GPUs fill be chosen from
from mpunet.utils import set_gpu
set_gpu(args.force_GPU)
if args.num_GPUs:
# Get GPU sets (up to the number of splits)
gpu_sets = get_free_GPU_sets(args.num_GPUs,
args.ignore_GPU)[:len(cv_folders)]
elif not args.num_jobs or args.num_jobs < 0:
raise ValueError("Should specify a number of jobs to run in parallel "
"with the --num_jobs flag when using 0 GPUs pr. "
"process (--num_GPUs=0 was set).")
else:
gpu_sets = ["''"] * args.num_jobs
# Get process pool, lock and GPU queue objects
lock = Lock()
gpu_queue = Queue()
for gpu in gpu_sets:
gpu_queue.put(gpu)
# Get file paths
script = os.path.abspath(args.script_prototype)
# Get GPU monitor process
running_processes, stop_event = start_gpu_monitor_process(
args, gpu_queue, gpu_sets, logger)
try:
for cv_folder in cv_folders[args.start_from:]:
gpus = gpu_queue.get()
t = Process(target=run_sub_experiment,
args=(cv_folder, out_dir, script, hparams_dir,
args.no_hparams, gpus, gpu_queue, lock, logger))
t.start()
running_processes.append(t)
for t in running_processes:
if not t.is_alive():
t.join()
except KeyboardInterrupt:
for t in running_processes:
t.terminate()
if stop_event is not None:
stop_event.set()
for t in running_processes:
t.join()
def run(args):
"""
Run the script according to args - Please refer to the argparser.
args:
args: (Namespace) command-line arguments
"""
from mpunet.logging import Logger
from utime.hyperparameters import YAMLHParams
from utime.utils.scriptutils import assert_project_folder
from utime.utils.scriptutils import get_splits_from_all_datasets
project_dir = os.path.abspath("./")
assert_project_folder(project_dir)
# Get logger object
logger = Logger(project_dir + "/preprocessing_logs",
active_file='preprocessing',
overwrite_existing=args.overwrite,
no_sub_folder=True)
logger("Args dump: {}".format(vars(args)))
# Load hparams
hparams = YAMLHParams(Defaults.get_hparams_path(project_dir),
logger=logger,
no_version_control=True)
# Initialize and load (potentially multiple) datasets
datasets = get_splits_from_all_datasets(hparams,
splits_to_load=args.dataset_splits,
logger=logger,
return_data_hparams=True)
# Check if file exists, and overwrite if specified
if os.path.exists(args.out_path):
if args.overwrite:
os.remove(args.out_path)
else:
from sys import exit
logger("Out file at {} exists, and --overwrite was not set."
"".format(args.out_path))
exit(0)
# Create dataset hparams output directory
out_dir = Defaults.get_pre_processed_data_configurations_dir(project_dir)
if not os.path.exists(out_dir):
os.mkdir(out_dir)
with ThreadPoolExecutor(args.num_threads) as pool:
with h5py.File(args.out_path, "w") as h5_file:
for dataset, dataset_hparams in datasets:
# Create a new version of the dataset-specific hyperparameters
# that contain only the fields needed for pre-processed data
name = dataset[0].identifier.split("/")[0]
hparams_out_path = os.path.join(out_dir, name + ".yaml")
copy_dataset_hparams(dataset_hparams, hparams_out_path)
# Update paths to dataset hparams in main hparams file
hparams.set_value(subdir='datasets',
name=name,
value=hparams_out_path,
overwrite=True)
# Save the hyperparameters to the pre-processed main hparams
hparams.save_current(
Defaults.get_pre_processed_hparams_path(project_dir))
# Process each dataset
for split in dataset:
# Add this split to the dataset-specific hparams
add_dataset_entry(hparams_out_path, args.out_path,
split.identifier.split("/")[-1].lower(),
split.period_length_sec)
# Overwrite potential load time channel sampler to None
split.set_load_time_channel_sampling_groups(None)
# Create dataset group
split_group = h5_file.create_group(split.identifier)
# Run the preprocessing
process_func = partial(preprocess_study, split_group)
logger.print_to_screen = True
logger("Preprocessing dataset:", split)
logger.print_to_screen = False
n_pairs = len(split.pairs)
for i, _ in enumerate(pool.map(process_func, split.pairs)):
print(" {}/{}".format(i + 1, n_pairs),
end='\r',
flush=True)
print("")
